Social Capital as an Instrument for Common Pool...

Social Capital as an Instrument for Common Pool Resource Management:Evidence from an Irrigation Management in Sri Lanka

PAC-DEV2011, March 12, 2011 @UC Berkeley

Takeshi Aida University of Tokyo / JICA Research Institute

E-mail: [email protected]

Outline

1. IntroductionBackground and objective

2. DataNatural & artefactual experiment

3. Empirical StrategyRegression model, potential reversed causality

4. ResultEffect of social capital on water allocation, merit from social capital

5. Conclusion2

CPR Management and Social Capital

Common Pool Resources (CPR): non-excludability & rivalness

⇒Tragedy of Commons (Hardin 1968) However, many empirical studies find that the tragedy

of commons are prevented even in the developing countries where the formal institutions are weak (Ostrom 1990, Aoki and Hayami 2001)

The key instrument is “social capital”

3

Heterogeneity Problem

However, CPR management is difficult when there is heterogeneity among the players

Heterogeneity in irrigation management (Bardhan and Dayton-Johnsn 2002):

- Income / wealth inequality- Head-enders and tail-enders- Exit options- Ethnic and social heterogeneity

4

Head-enders / Tail-enders Problem

If the head-enders draw too much water, the tail-enders cannot use enoughcollective action between head and tail is difficult

The difference in water-availability between head and tail leads to the lower management of irrigation (Fujiie et al 2005)

5

Goal of this Study

The goal of this study is to show the effect of social capital on water allocation between head-enders and tail-enders

Contribution

Use social capital variable measured by dictator and trust game

Focus on water allocation problem, not maintenance

←use subjective assessment of water usage Use natural experimental situation to eliminate

the effect of wealth inequality→Head and tail problem may simply reflect the

wealth inequality (Bardhan and Dayton-Johnson 2002)

7

Data

Impact Assessment of Infrastructure Development on Poverty Alleviation in Sri Lanka conducted by JICA/JBIC

Study site: Walawe Left Bank (WLB) in southern part of Sri Lanka

8

Study Site

The government started to construct irrigation canal in 1997, using the aid from JBIC

Five blocks: Sevenagala Irrigated, Sevanagala Rainfed, Sooriyawewa, Kiriibanwewa, Extension Area

Each block has distribution canals (D-canal) Irrigation management is conducted by each D-

canal

9

Natural Experimental Situation

Allotment of irrigation plot is partly determined by lottery mechanism

The property right of the irrigation plot was not given to farmers until 2009. Selling the plot is still prohibited even after 2009head and tail asymmetry is not affected by income or asset inequality

10

CDF of Income and Irrigated Plot Size

There is no systematic difference between head-end and tail-end

11

Artefactual Experiment

Experiment conducted in March 2009 Measuring social capital by using- Dictator game- Trust game Measuring risk attitude by dice game

12

Dictator and Trust Game

Initial endowment was Rs. 500 A first mover decides how much to send his/her four type

of partners:- Three onymous people in the same D-canal- An anonymous person in the same D-canal- An anonymous person in different D-canal but in the

same block- An anonymous person in the different block In trust game, the amount is tripled when the second

mover received and he/she decides how much to return to the first mover

13

Social Capital to Head/Tail-Enders

In dictator game and trust game, players can identify whether their partners are in the head/tail area compared to themselvesthe cross terms of the game results and “vs_head/tail” dummy capture the social capital to the head-enders/tail-enders

Note that incentive structure of these games are closely resembles to the one for head-enders in irrigation water allocation

14

Empirical Strategy

Dependent variable:- Whether they were satisfied with their water usage- What percentage of water they could use

compared to the amount they wanted Satisfaction is determined by the difference

between the water supply and their water demandIf we control water supply, people with less water demand are more likely to be satisfied with water

15

Controlling Water Supply

Water supply is determined for each distribution canalwater supply can be controlled by including distribution canal dummies

They also capture the difference of income inequality among the distribution canals

16

Estimated Model

Satisfactioni=β0+β1SCij+β2SCij×vs_tailij

+β3SCij×vs_headij+Xiδ+DCi+εij There may be reversed causality problem

between satisfaction and social capitalneed for instrumental variable

Game results to someone in different D-canal can be used because they do not face the same CPR management problem

17

Sample

The sample is restricted to paddy farmers- Paddy is the most popular crop in the study site- Paddy requires a lot of water when its growing

seasonWater allocation problem is the most serious for this crop

18

Summary Statistics

19

Results: Dictator Game

20

Results: Trust Game

21

Interpretation

Vs_tail×game results are positive and significantstronger social capital to tail leads to satisfaction with water usageconsistent with hypothesis

Coefficient on endogenous variable are larger in IV than in OLSsatisfaction affects negatively on social capitalconsistent with Hayami (2009)

22

Further Investigation

Why trust to tail-enders has more robust effect on satisfaction?

Since the behavior in trust game expects benefit from trustee, there may be some positive return from social capital

Merit from social capital can be explained in terms of risk-sharing

23

Risk-Sharing

Merit from risk-sharing can be larger when there is locational variation among members.

In addition, belonging to the same D-canal decrease the monitoring cost because they are embedded in the situation

24

Risk-Sharing Group Formation

Based on Fafchamps and Gubert (2007), dyadic regression model is estimated for local microfinance group formation

25


26


Risk-sharing group is formed among people in the different part of the canalSignificant merit for head-enders from trusting their tail-enders

27

Conclusion

Social capital, especially trust, between head-enders and tail-enders can lead to better CPR management.

Since incentive for head-enders closely resembles to dictator or trust game, the results also show the validity of these games

Merit from social capital between head-enders and tail-enders can be explained in terms of risk-sharing

28

Model (Aoki 2001)

Linked game: - irrigation management game {Cooperate, Shirk}- social exchange game Irrigation access is non-excludable Players can punish a player who neglects the

irrigation management by excluding him/her from social exchange gameIf the benefit from social capital is enough large, the players refrain from selfish behavior

29

Appendix: Paddy Production Function with Water Satisfaction

Transport Costs and Economic Geography:Evidence from Indonesia’s Highways

Alexander D. RothenbergUC Berkeley

March 12, 2011

Transportation and Regional Development

Classic Question: How do transportation improvements affect thegrowth of different regions?

I Important for assessing effects of transport policies indeveloping countries.

I Policy view: improvements in transport infrastructure:

“... support economic growth, national stability, and theequitable distribution and dissemination of developmentefforts, penetrating the isolation and backwardness ofremote areas, to further strengthen the Archipelago ...”

− Planning documents, Repelita VI.

Theory and Empirics: Unsettled

Theory delivers conflicting predictions.

I Agglomeration (Krugman, 1991)?

I Dispersion (Helpman, 1998)?

Empirical work suffers from identification problems:

I Improvements are not randomly assigned (targeting bias).

I Location characteristics both determine and are affected byfirms choices (simultaneity).

“[E]mpirical studies still fall short of the theoretical research”− Combes, Mayer, and Thisse (2008)

Overview of Methodology

I Study the location choices of new manufacturing plants inresponse to large road improvements in Indonesia in the1990s.

I Construct new panel data on road quality and transportcosts.

I Combine with rich data on Indonesian manufacturing plants,other location characteristics.

Identification and Estimation: Reduced Form

Exploit aspects of Indonesia’s road improvement program:

I Infrequent changes to national spatial plans.

I Long term planning involved in road improvements.

I Randomness in the timing of implementation.

1. Reduced form estimates:I Fixed effects (region, sector, and year) remove targeting bias.

I Variation in treatment intensity across industrial sectors.

Firm Location Choices

2. Structural Model: monopolistic competition and regionaltrade.

3. Estimate location choice probabilities using BLP techniques.I Instrument for the endogenous choice characteristics.

I Use different sets of instruments (weak exogeneity, costshifters).

4. Simulate counterfactual outcomes.

Outline

1. Background and Data

2. Reduced Form Results

3. Conclusion: (brief discussion of BLP results).

Manufacturing and Transport in the 1990s

I Liberalization policies led to major growth in Indonesianmanufacturing.

I In the late 1980s, an 83 percent increase in fundingallocated for road improvements.

I Transportation is nearly 20% of the budget in Repelita V(1989-1994), single largest item.

Data: Road Quality

I New panel data on the quality of Indonesia’s highways.

I Data produced by Indonesia’s Department of Public Works.

I Extremely detailed: kilometer-post intervals of majorinter-urban roads.

I Original dataset contains ≈ 1.2 million obs. from 1990-2007.I All nationally managed highways in Indonesia.I Measures: surface type, width, and road roughness.

I Use roughness-induced speed limits to estimate traveltimes.

Sumatra: Road Quality

I 32% of the network is paved.

Transport Costs

I Average reduction in travel times (1990-2000):I Java: 17 percent.

I Sumatra: 24 percent.

I Sulawesi: 38 percent.

I Substantial spatial variation in the timing and extent of roadimprovements.

Spatial Concentration over TimeI ≈ 25% decrease in average spatial concentration (1985-1996).

Source: SI data and author’s calculations. Lines depict annual means or medians of different indices ofindustrial concentration across 5-digit industries.

Differences Across Industries

I Perishable Goods: Less sensitive to transport costimprovements.

I Very perishable goods cannot move away from their market.

I Durable goods can move away if transport costs are sufficientlylow.

I Use inventories / output as a proxy for durability.

I Example: Food and beverage production:I Tofu, tempe, and ice: Increases in concentration.I Coconut and palm oil, canned seafood: Reductions in

concentration.

Spatial Concentration: Durables vs. Non-DurablesI Change in treated spatial concentration measure relative to

control group (1985-1996): ≈ -0.05, or -0.8σ.

Source: SI data and author’s calculations. Lines depict annual means or medians of different indices ofindustrial concentration across 5-digit industries.

New Firm Shares by Type of Region: Durables vs.Non-Durables

Source: SI data and author’s calculations.

Reduced Form RegressionsOverall Effect: Fixed Effects

yrjt = βMPrt + γr + γj + γt + εrjt

(γr + γjt)

Differential Effect: Within region-year differences in treatmentintensity.

yrjt = δ (MPrt × Tj) + γrt + γjt + εrjt

I r : regions

I j : sectors (industries)

I t: time

I MPrt : market potential in region r , time t

I Tj : indicator for durable goods producing industries

I yrjt : outcomes (new firms, employment)

Reduced Form Regressions

Panel A: New Firms (1) (2) (3)

log MP 0.589 0.589(0.149)*** (0.149)***

log MP × Treated 0.078(0.011)***

Adj. R2 0.097 0.100 0.082N 359264 359264 352672Kabupaten FE Yes Yes .Year FE Yes . .Sector FE Yes . .Kabu-Year FE . . YesSector-Year FE . Yes Yes

Unit of observation is a region-industry-year. Robust standard errors in parentheses, clustered at thekabupaten level. * denotes significant at the 10% level, ** denotes significant at the 5% level, and *** denotessignificant at the 1% level.

Reduced Form Regressions

Panel B: Employment (1) (2) (3)

log MP 3.776 3.776(0.829)*** (0.830)***

log MP × Treated 0.280(0.041)***

Adj. R2 0.096 0.102 0.093N 359264 359264 352672Kabupaten FE Yes Yes .Year FE Yes . .Sector FE Yes . .Kabu-Year FE . . YesSector-Year FE . Yes Yes

Unit of observation is a region-industry-year. Robust standard errors in parentheses, clustered at thekabupaten level. * denotes significant at the 10% level, ** denotes significant at the 5% level, and *** denotessignificant at the 1% level.

Structural Model: (Preliminary) Results

I Reduced form estimates have no policy interpretations =⇒need a model for richer predictions.

I Model intuition: firms trade off market access and factorprices.

I Estimation: Random coefficient logit model with endogeneity(BLP).

I Want a model that delivers rich substitution patterns.

Cross Market-Potential Elasticities

ηjkt =∂ log sjt

∂ log MPkt

Source: Author’s calculations.

Conclusion

I Road improvements in Indonesia have resulted in a modestdispersion of manufacturing activity.

I Large differences in sensitivities to improved market access byindustrial sector.

I (Preliminary) counterfactual simulations: suburbanization ofindustry.

Ongoing work

1. Structural Model

2. Random Coefficients Logit Estimation

3. Counterfactual Simulations

1

Does Infrastructure Facilitate Social Capital Accumulation?

Evidence from Natural and Artefactual Field Experiments in Sri Lanka

Keitaro Aoyagi JICA

Ryuji Kasahara JICA

Masahiro Shoji Seijo University

Yasuyuki Sawada University of Tokyo

JICA RI

2

1 Motivation and Background

Natural Experiment

Field Experiments

Descriptive statistics

Regression Analysis

2

3

4

5

1. Motivation and Background

3


Impacts of infrastructure on social capital (SC) unknown

“Impacts” of SC have been known (Ostrom, 1990; Karlan, 2005)

“Determinants” are largely unknown (Miguel, Gertler, & Levine,

2006):

The Aoki-Hayami hypothesis on the role of irrigation in

accumulating SC (Aoki, 2001; Hayami, 2001) and the “linked

game” (Aoki, 2001)

We aim to bridge the gap in the literature by examining a unique

situation of an irrigation project in Sri Lanka

The role of irrigation infrastructure in “determining” the level of

SC

4

A Preview of Findings

1. Across communities, spatial and relational distance explain SC

Trusting behavior is also affected by expected returns

(consistent w/ Barr, 2003).

2. Within-community variation in SC is driven more by the years of

access to the irrigation infrastructure

3. A physical access to irrigation infrastructure is one of the most

important determinants of SC. Social preference may emerge from

institutional environment given by physical access to irrigation.

“Embedded” people’s behavior is physically disciplined by

irrigation infrastructure.

Different from cooperation arising from repeated interactions

5


We investigate the role of irrigation infrastructure in

“determining” the level of SC

Two technical difficulties:

A) Difficult to identify the “causal” impact of irrigation

on SC (Duflo and Pande, 2007)

B) Difficult to elicit the level of SC (Gaeser et al., 2000)

Irrigation → SC → Socio-economic outcomes

6


A) To identify the “causal” impact of irrigation on SC,

We employ a “natural” experiment of an irrigated area in

Southern Sri Lanka where:

Irrigation was newly constructed in previously

undeveloped area

A significant portion (1/4) of irrigated land

distribution has been made through a lottery or

effectively exogenous mechanisms; also, conditional

independence tests passed

Homogenous land distribution

7

B) To elicit the level of SC,

Artefactual economic experiments:

Carefully designed field experiments have become a

common research tool (Carpenter and Cardenas,

2008; Harrison and List, 2004)

We employ trust game (Berg et al., 1985), dictator

game, and risk game (and public goods game).


8

1 Motivation and background

Natural Experiment

Field Experiments


Regression Analysis

2

3

4

5

2. Natural Experiment

9


Irrigation infrastructure has been

extended from the North to the

South.

Phase I (green part) Initiated in 1995

Completed the improvement

(2,900ha) and extension (1,100ha)

of irrigation systems (442km) in

2001 (60-70%)

Phase II (pink part) Extension of irrigation, completed

in Dec 2008

In total, 940 km canals, covering

9,000 ha

Walawe Left Bank Upgrading and Extension Project

10

Figure 2 Sampling Scheme

11


10/33

Maha Yala Maha Yala Maha Yala Association Experiments Maha

(Rainy) (Dry) (Rainy) (Dry) (Rainy) (Dry) (Rainy)

Oct

2000

May

2001

July

2001

Oct

2001

May

2002

Sep

2002

July

2007

Oct-Nov

2007

Jan

2009

March

2009

May

2009

Round 1 2 3 4 5 6 7 8 9 10

Data: The final round of 10-wave panel data

5 rounds panel data of 858 households (including “before” group)

2001～2002

5 follow-up surveys (including “after” of the original control group)

2006-07 Maha; 2007 Yala

Jan 2009 (institution survey)

March 2009 (field experiments)

May 2009 (HH survey for the experiments)

12


Natural Experiment

Field Experiments


Regression Analysis

2

3

4

5

3. Artefactual Field Experiments

13

Sooriyawewa

Total HH-914

Selected HH-36

Extension Area

Total HH-518

Selected HH-105

Walawe Left Bank

Total HH-16800

Selected HH-858

Sevanagala

Total HH-1764

Selected HH-34

Sevanagala Irrigated

Total HH-696

Selected HH-25

C 2 (Head)- 09

C 8 (Tail)- 14

Bellagaswewa-11

Bolhida -4

Wediwewa-19

Mahaara-24

Andarawewa- 47

BBSB 2- 08

BBD 7- 04

BBD 5- 09

BBD 2- 05

MD 18 -07

MD 15 - 03

MD 10-04

MD 3- 02

MD 14- 06

MD 11- 03

MBD 2- 01

KWLB- 05

Sevanagala Rainfed

Total HH-665

Selected HH-09

Rainfed Div 1-05

Rainfed Div 3-04

Kiriibbanwewa

Total HH-782

Selected HH-22

You are here

Social (Spatial) Distance

3. Artefactual Field Experiments (setting)

14

Sooriyawewa

Total HH-914

Selected HH-36

Extension Area

Total HH-518

Selected HH-105

Walawe Left Bank

Total HH-16800

Selected HH-858

Sevanagala

Total HH-1764

Selected HH-34


Total HH-696

Selected HH-25

C 2 (Head)- 09

C 8 (Tail)- 14

Bellagaswewa-11

Bolhida -4

Wediwewa-19

Mahaara-24

Andarawewa- 47

BBSB 2- 08

BBD 7- 04

BBD 5- 09

BBD 2- 05

MD 18 -07

MD 15 - 03

MD 10-04

MD 3- 02

MD 14- 06

MD 11- 03

MBD 2- 01

KWLB- 05

Sevanagala Rainfed

Total HH-665

Selected HH-09

Rainfed Div 1-05

Rainfed Div 3-04

Kiriibbanwewa

Total HH-782

Selected HH-22

Known person or Someone in your distribution canal


I

15

Sooriyawewa

Total HH-914

Selected HH-36

Extension Area

Total HH-518

Selected HH-105

Walawe Left Bank

Total HH-16800

Selected HH-858

Sevanagala

Total HH-1764

Selected HH-34


Total HH-696

Selected HH-25

C 2 (Head)- 09

C 8 (Tail)- 14

Bellagaswewa-11

Bolhida -4

Wediwewa-19

Mahaara-24

Andarawewa- 47

BBSB 2- 08

BBD 7- 04

BBD 5- 09

BBD 2- 05

MD 18 -07

MD 15 - 03

MD 10-04

MD 3- 02

MD 14- 06

MD 11- 03

MBD 2- 01

KWLB- 05

Sevanagala Rainfed

Total HH-665

Selected HH-09

Rainfed Div 1-05

Rainfed Div 3-04

Kiriibbanwewa

Total HH-782

Selected HH-22

Someone in a different DC, but in your block


II

16

Sooriyawewa

Total HH-914

Selected HH-36

Extension Area

Total HH-518

Selected HH-105

Walawe Left Bank

Total HH-16800

Selected HH-858

Sevanagala

Total HH-1764

Selected HH-34


Total HH-696

Selected HH-25

C 2 (Head)- 09

C 8 (Tail)- 14

Bellagaswewa-11

Bolhida -4

Wediwewa-19

Mahaara-24

Andarawewa- 47

BBSB 2- 08

BBD 7- 04

BBD 5- 09

BBD 2- 05

MD 18 -07

MD 15 - 03

MD 10-04

MD 3- 02

MD 14- 06

MD 11- 03

MBD 2- 01

KWLB- 05

Sevanagala Rainfed

Total HH-665

Selected HH-09

Rainfed Div 1-05

Rainfed Div 3-04

Kiriibbanwewa

Total HH-782

Selected HH-22

Someone in different blocks (units)


III

17

3. Artefactual Field Experiments (Sampling Scheme)

Walawe Left Bank

Selected HH-268

Block 4

Sooriyawewa

Selected HH-40

BBSB 2- 08

BBD 7- 08

BBD 5- 08

BBD 2- 04

MD 18 -08

MD 15 - 04

Block 4

Sooriyawewa

Selected HH-40

BBSB 2- 08

BBD 7- 08

BBD 5- 08

BBD 2- 04

MD 18 -08

MD 15 - 04MD 15 - 04

Block 3

Kiriibbanwewa

Selected HH-32

MD 10-04

MD 3- 04

MD 14- 08

MD 11- 04

MBD 11- 04

KWLB- 08

PD2- 04

UD6- 20

UD7- 04

UD8- 04

PD10/11- 04

PD12- 04

UD13-04

PD14- 08

UD15- 16

PD16- 12

PD17- 08

PD21- 12

PD22- 04

UD23- 04

HT9- 04

UD27- 04

UD29- 04

PDL32- 04

PDR32- 04

UD41- 04

PD47- 04

UD56- 08

PD57- 04

PD58- 04

Sevanagala

Selected HH-40

Block 1

Sevanagala

Irrigated

Selected HH-28

C 2 (Head)- 12

C 8 (Tail)- 16

Block 2

Sevanagala

Rainfed

Selected HH-12

Rainfed Div 1-08

Rainfed Div 3-04

Sevanagala

Selected HH-40

Block 1

Sevanagala

Irrigated

Selected HH-28

C 2 (Head)- 12

C 8 (Tail)- 16

Block 2

Sevanagala

Rainfed

Selected HH-12

Rainfed Div 1-08

Rainfed Div 3-04

Block 2

Sevanagala

Rainfed

Selected HH-12

Rainfed Div 1-08

Rainfed Div 3-04

Block 5

Extension Area

Selected HH-156

18

ID Rs.0 Rs.50 Rs.100 Rs.150 Rs.200 Rs.250 Rs.300 Rs.350 Rs.400 Rs.450 Rs.500Expected

Returen

1

2

3

4

5

6

Name

someone in your

distribution canal

someone in a different

distribution, but in your unit

someone in different unit

In our setting,

Initial endowment = Rs 500.

The strategy method were used (and chosen randomly for

actual payments).

Potential partners are constructed based on social (spatial)

distance.

In the case of the same DC, each participant can identify

the three partners’ face (& name)

3. Artefactual Field Experiments α: Trust Game

I

II

III

19

ID Rs.0 Rs.50 Rs.100 Rs.150 Rs.200 Rs.250 Rs.300 Rs.350 Rs.400 Rs.450 Rs.500Expected

Returen

1

2

3

4

5

6

Name

someone in your

distribution canal

someone in a different

distribution, but in your unit

someone in different unit

In our setting,


The strategy method were used.

Q. “How much do you pass to the counterpart. Please

check the box for each.”

Altruism (or social image in Andreoni and Bernheim, 2008)

3. Artefactual Field Experiments β: Dictator Game

III

II

I

20

To elicit the degree of risk aversion, we conduct a

risk game.

In our setting,


Make an investment out of the endowment

Return will be chosen randomly

3. Artefactual Field Experiments γ: Risk (Dice) Game

21

4. Descriptive Statistics (Trust Game)

Fig. 3 CDFs of Transfers in Trust Game

22


Natural Experiment

Field Experiments


Regression Analysis

2

3

4

5

5. Regression Analysis

23

5. Regression analysis across communities

Table 7 Determinants of Social Capital with “identified” Sample (lottery subsample)

Dependent variable: Amount sent in the trust game (1) (2) (4)

Method pooled OLS Block FE Individual FE

With the “identified” sample? YES YES YES

With the “identified” variable? YES YES YES

Irrigation Access Year 2.434 3.073 [1.349]* [0.818]** Identified * Irrigation Access Year 1.539 1.539 1.562 [1.676] [0.490]** [1.694] Same DC * Irrigation Access Year -1.057 -1.058 -1.107 [1.429] [2.146] [1.474] Different DC * Irrigation Access Year -0.999 -0.999 -0.957 [0.880] [1.159] [0.862] Identified 69.686 69.719 73.414 [20.642]*** [13.022]*** [20.703]*** Same DC 37.685 37.713 40.806 [20.499]* [30.006] [20.107]** Different DC 33.660 33.667 34.432 [14.247]** [10.696]** [14.006]** Dictator Game 0.537 0.536 0.485 [0.095]*** [0.046]*** [0.091]*** Risk Game 0.146 0.120 [0.103] [0.102] Household Head 12.034 7.401 [27.775] [45.447] Age -0.386 -0.251 [0.780] [0.277] Sex (1 = male) -19.996 -14.378 [29.983] [24.902] Education (highest grade completed) 1.127 1.206 [2.896] [2.084] Constant 14.985 5.967 -1.107 [54.914] [46.843] [1.474] Number of observations

378 378 402

Adjusted R-squared

0.4474 0.4441 0.394

Note: Robust standard errors in brackets. In column (1),and (5), cluster-adjusted robust standard errors are reported.

* significant at 10% level; ** significant at 5% levell; *** significant at 1% level

24

5. Regression analysis w/ dyadic data of known people

Table 9 Determinants of Social Capital using Dyadic Data (lottery subsample)

Dependent variable: the amount sent in the trust game

(1) (2) (3) (4) (5) (6) (7) (8)

Method pooled OLS

Block FE DC FE Indi. FE pooled OLS Block FE DC FE Indi. FE

Irrigation Access Year (IRR) 3.973 5.644 2.388 3.867 5.428 2.572 [1.568]** [2.440]* [1.717] [1.528]** [1.628]** [1.294]*

Respondent characteristics (Xi) Dictator Game 0.499 0.533 0.468 0.547 0.491 0.593 0.508 0.448 [0.154]*** [0.103]*** [0.077]*** [0.152]*** [0.160]*** [0.126]*** [0.081]*** [0.131]*** Risk Game 0.178 0.088 0.096 0.259 0.145 0.230 [0.129] [0.109] [0.151] [0.129]* [0.057]* [0.132]* Household Head 9.362 -12.060 -82.981 13.465 -26.202 -105.332 [40.048] [31.758] [34.806]** [42.411] [32.935] [35.979]*** Age -0.059 0.274 2.856 0.183 0.980 3.268 [1.118] [0.958] [1.022]*** [1.115] [0.454]* [0.846]*** Sex (1 = male) -20.654 -8.077 5.345 -20.549 1.600 1.427 [40.963] [16.952] [36.966] [39.464] [19.597] [38.597] Education (highest grade completed)

1.286 -0.538 -0.431 -0.139 -4.050 -0.878

[4.239]

[3.008] [2.756] [4.487] [3.010] [3.082]

Receiver characteristics (Xj) YES YES YES YES YES YES YES YES Bilateral relationships (Dij) SHORT SHORT SHORT SHORT FULL FULL FULL FULL

Constant 84.947 73.509 -43.256 28.176 9.391 -0.661 -100.154 43.286 [111.709] [75.924] [85.792] [67.008] [114.804] [42.471] [93.414] [68.684] Number of Observations 163 163 163 163 160 160 160 160 Adjusted R-squared 0.32 0.35 0.36 0.26 0.39 0.45 0.40 0.24 Joint significance of receiver characteristics (P-values)

0.6672 0.0008 0.4982 0.5276 0.6272 0.0000 0.1664 0.5430

Joint significance of bilateral relationships (P-values)

0.5370 0.1360 0.0162 0.7464 0.0020 0.0034 0.0014 0.0667

Joint significance of receiver characteristics and bilateral relationships (P-values)

0.6633 0.0010 0.0033 0.7661 0.0017 0.0235 0.0001 0.1372

Note: Robust standard errors in brackets. In column (1), cluster-adjusted robust standard errors are reported. * significant at 10% level; ** significant at 5% level; *** significant at 1% level

25

5. Regression analysis w/ expected returns from the game Table 10 Determinants of Social Capital and Expected Returns (lottery subsample)

Dependent variable: Amount sent in the trust game

Method pooled OLS Block FE DC FE Individual FE

Irrigation Access Year 2.406 3.084 0.640 [1.307]* [0.842]** [1.195] Expected Trustworthiness 98.665 104.519 87.363 27.498 [30.475]*** [15.906]*** [35.486]** [35.852] Identified * Irrigation Access Year 1.283 1.268 1.334 1.487 [1.641] [0.664] [1.863] [1.644] Same DC * Irrigation Access Year -1.177 -1.184 -1.209 -1.133 [1.538] [2.020] [1.717] [1.481] Different DC * Irrigation Access Year -1.030 -1.034 -0.971 -0.980 [0.891] [0.839] [0.770] [0.852] Identified 62.109 61.660 66.364 70.750 [21.682]*** [16.218]** [22.242]*** [22.879]*** Same DC 28.440 27.892 32.335 37.767 [21.697] [28.207] [21.865] [22.180]* Different DC 26.148 25.765 27.273 32.442 [16.157] [11.755]* [14.655]* [15.971]** Dictator Game 0.463 0.458 0.424 0.472 [0.090]*** [0.048]*** [0.058]*** [0.088]*** Risk Game 0.154 0.125 0.141 [0.094] [0.081] [0.144] Household Head 0.514 -5.100 -68.714 [26.499] [39.361] [42.352] Age -0.098 0.074 2.231 [0.768] [0.226] [1.500] Sex (1 = male) -16.917 -11.445 13.395 [28.264] [23.432] [37.626] Education (highest grade completed) 1.000 1.234 2.474 [2.817] [1.435] [3.348] Constant -17.032 -29.370 -82.717 53.305 [53.038] [32.883] [99.897] [12.603]*** Number of observations 377 377 377 377 Adjusted R-squared 0.45 0.46 0.45 0.47

26

Robustness checking

1. Income or wealth effect by staying longer years, rather

than the physical environment?

• Inclusion of income does not change the qualitative

results

2. Repeated interaction, rather than the physical

environment?

• Years of sharing the same DC are not significant

• Public goods game (multi-person PD) results are not

sensitive to the years variable (as a byproduct, PG

results are sensitive to DG and risk results)

27

Summary of Findings

1. Across communities, spatial and relational distance explain the trust

(SC).

“Linked game” mechanism? (Aoki, 2001).

Trusting behavior is affected by expected returns (consistent w/

Barr, 2003).

2. Within-community variation in SC is driven more by the years of

access to the irrigation infrastructure

3. A physical access to irrigation infrastructure is one of the most

important determinants of SC. Social preference may emerge from

institutional environment given by physical access to irrigation.

“Embedded” people’s behavior is physically disciplined by

irrigation infrastructure.

Different from cooperation arising from repeated interactions

28

JICA Research Institute’s Impact Evaluation Project

JICA Research Institute has been working on

quantitative impact evaluation of JICA projects:

1. Community-managed school project in Burkina Faso

(RCT and artefactual field experiments)

2. Mother and Child Health (MCH) Handbooks in Indonesia

and Palestine (Non-experimental evaluation)

etc…

Bright Lines, Risk Beliefs, and Risk Avoidance:Evidence from a randomized Intervention in

Bangladesh

Lori BennearAlessandro TarozziAlexander PfaffH. B. Soumya

Kazi Matin AhmedAlexander van Geen

image004 (130x150x256 gif)

March 2011

Bennear, Tarozzi et al. (Duke etc.) Framing/Arsenic/Bangladesh March 2011 1 / 10

Background

1970s: UN/World Bank/Bangladesh Government encourage switching togroundwater to reduce bacterial disease from surface water.

1980s: High levels of arsenic (As) discovered in groundwater in WestBengal, 1990s in Bangladesh.

Naturally occurring, odorless and colorless.Haphazard distribution (even within villages), often uncorrelated withsocio-economic status.Chronic exposure associated to skin lesions, cancer, cardiological andneurological diseases, diabetes, all cause mortality.

Bangladesh Arsenic Mitigation Water Supply Program (BAMWSP)conducts widespread testing in 1999-2005.

Wells were painted red if above 50 ppb and green if less 50 ppb.∼ 5m wells tested. ∼ 30% labeled “unsafe” and painted red.

However, the As problem remain partly unresolved: not all wells weretested and new wells are dug annually. ∼1/3 of wells used for drinking areuntested.


Solutions in Rural Areas

Deep, community wells, piped water, return to surface water.

Water treatment.

Information → Switching to safer wells.

Caveat: switching to shallower, more remote wells may bring backdiarrheal disease (van Geen et al 2010, Field et al 2011)


Motivation: Information as a Policy Tool

Growing literature in economics on behavioral responses to provisionof health-related information in developing countries

Coates et al. 2000, Goldstein et al. 2010, Thornton 2008, Dupas 2010,Gong 2010 on HIV, Jalan and Somanathan 2008 on water quality.

Columbia U Superfund, Araihazar district

∼30-60% switching out of unsafe wellsconsequent substantial reductions in As exposure & health risks

However, “binary” information may not be ideal, e.g.:

Promotes switch from 60 to 30 ppb, but not from 400 to 200 ppb,despite the latter leading to larger risk reductionsDoes not promote a switch from 40 to below WHO standards (10ppb)

Can richer information led to ‘better switches’?


Study Area in Araihazar and Construction of Sample

Araihazar

~1 Km.

Safe and Unsafe wellsin study area

Water samples from untested wells in 43 villages, Schoenfeld 2005.

February-April 2008: Delivery of results and survey of owners/users.

November 2008: Follow-up survey:after ∼10% attrition (balanced), 376 wells, 533 households, 43 villages.


Randomized emphasis on ‘gradient’ when delivering results

“The national safety standard in Bangladesh is 50 ppb. That means thefederal government says drinking water with more than 50 ppb arsenic isnot safe.”

Bright Line (Binary only)“When possible you should seek to fetch drinking water from a well that is labeled safe.”

+Gradient“However, we want to emphasize that whatever the level of arsenic in your drinking waternow, if you have a choice of water from several wells it is better to drink water from thewell with the lowest level of arsenic. For example, if you have a choice between a well with200 ppb arsenic and a well with 100 ppb arsenic, drinking water from the well with 100ppb arsenic is better for you. If you have a choice between a well with 40 ppb arsenic anda well with 10 ppb arsenic, drinking water from the well with 10 ppb arsenic is better foryou. When possible you should seek to fetch drinking water from the well with the lowestarsenic level.”


Summary Statistics and Tests of Cross-arm Balance

(1) (2) (4) (5) (6)Obs Mean Std Dev Gradient p-value

−Binary

Well Arsenic Level

J

533 116 149 17 0.60Unsafe (As > 50 ppb)

J

533 0.61 0.49 -0.06 0.62# Household members 533 5.30 2.50 0.09 0.75Household head: woman 525 0.19 0.39 -0.05 0.15Number of adult females 533 1.70 0.89 -0.11 0.25Number of children 533 3.10 1.60 -0.05 0.78% members age > 10 who can read/write

J

533 0.55 0.33 -0.02 0.68% of 6-14 in a household in school 375 0.76 0.36 -0.02 0.61Value of food consumed: monthly

J

530 5073 3339 507 *0.07Total expenditure: monthly

J

527 5942 3227 371 0.16Typical income: monthly

J

495 7672 7193 515 0.40Expenditure on medicines: monthly

J

510 543 1227 -159 0.13% members with symptoms of arsenic poisoning

J

533 0.01 0.07 0.00 0.69Any member with symptoms of arsenic poisoning

J

533 0.04 0.19 0.02 0.56Mean # days of illness last year (ages 6 to 70)

J

532 19 22 -0.23 0.91Household owns well

J

527 0.7 0.46 -0.019 0.60Distance to nearest well below 50 ppb

J

529 75 219 55 0.40Distance to nearest safer well

J

531 86 137 -8.10 0.58Average concern about long-term arsenic risk (1/10)

J

510 7.8 1.6 0.098 0.64




−Binary

Well Arsenic LevelJ 533 116 149 17 0.60Unsafe (As > 50 ppb)J 533 0.61 0.49 -0.06 0.62# Household members 533 5.30 2.50 0.09 0.75Household head: woman 525 0.19 0.39 -0.05 0.15Number of adult females 533 1.70 0.89 -0.11 0.25Number of children 533 3.10 1.60 -0.05 0.78% members age > 10 who can read/write

J


J


J


J


J


J


J


J


J


J


J


J

510 7.8 1.6 0.098 0.64




−Binary

Well Arsenic Level

J

533 116 149 17 0.60Unsafe (As > 50 ppb)

J

533 0.61 0.49 -0.06 0.62# Household members 533 5.30 2.50 0.09 0.75Household head: woman 525 0.19 0.39 -0.05 0.15Number of adult females 533 1.70 0.89 -0.11 0.25Number of children 533 3.10 1.60 -0.05 0.78% members age > 10 who can read/writeJ 533 0.55 0.33 -0.02 0.68% of 6-14 in a household in school 375 0.76 0.36 -0.02 0.61Value of food consumed: monthly

J


J


J


J


J


J


J


J


J


J


J

510 7.8 1.6 0.098 0.64




−Binary

Well Arsenic Level

J

533 116 149 17 0.60Unsafe (As > 50 ppb)

J


J

533 0.55 0.33 -0.02 0.68% of 6-14 in a household in school 375 0.76 0.36 -0.02 0.61Value of food consumed: monthlyJ 530 5073 3339 507 *0.07Total expenditure: monthlyJ 527 5942 3227 371 0.16Typical income: monthlyJ 495 7672 7193 515 0.40Expenditure on medicines: monthly

J


J


J


J


J


J


J


J

510 7.8 1.6 0.098 0.64




−Binary

Well Arsenic Level

J

533 116 149 17 0.60Unsafe (As > 50 ppb)

J


J


J


J


J

495 7672 7193 515 0.40Expenditure on medicines: monthlyJ 510 543 1227 -159 0.13% members with symptoms of arsenic poisoningJ 533 0.01 0.07 0.00 0.69Any member with symptoms of arsenic poisoningJ 533 0.04 0.19 0.02 0.56Mean # days of illness last year (ages 6 to 70)J 532 19 22 -0.23 0.91Household owns well

J


J


J


J

510 7.8 1.6 0.098 0.64




−Binary

Well Arsenic Level

J

533 116 149 17 0.60Unsafe (As > 50 ppb)

J


J


J


J


J


J


J


J


J

532 19 22 -0.23 0.91Household owns wellJ 527 0.7 0.46 -0.019 0.60Distance to nearest well below 50 ppbJ 529 75 219 55 0.40Distance to nearest safer wellJ 531 86 137 -8.10 0.58Average concern about long-term arsenic risk (1/10)J 510 7.8 1.6 0.098 0.64


Switching Rates vs. Message Format

All

All Unsafe Unsafe

Intercept 0.07 (.027)***

0.29 (.035)*** 0.23 (0.06)*** 0.28 (0.19)***

+Gradient added

−0.09 (.05)* 0.12 (0.11) 0.11 (0.07)

Unsafe (As> 50ppb) 0.28 (.043)***

− −

High As (As> 100ppb)

0.24 (0.07)*** .22 (0.05)***

Gradient×High As

−0.30 (0.12)** −0.26 (0.10)**

Controls & strata dummies No

No No Yes

Observations 533

533 326 308

Clusters 43

43 42 41

All results robust to intra-village correlation

Overall, less switching with ‘gradient emphasis’

‘Gradient emphasis’ among unsafe wells:

12pp more switching (not sign.) for moderately unsafe wells (50-100)but 30pp less switching (sign. 5%) for highly unsafe wells (As>100)

Very similar results with controls (distance from safer well etc.) & strata dummies



All All

Unsafe Unsafe

Intercept 0.07 (.027)*** 0.29 (.035)***

0.23 (0.06)*** 0.28 (0.19)***

+Gradient added −0.09 (.05)*

0.12 (0.11) 0.11 (0.07)

Unsafe (As> 50ppb) 0.28 (.043)***

− −

High As (As> 100ppb)

0.24 (0.07)*** .22 (0.05)***

Gradient×High As

−0.30 (0.12)** −0.26 (0.10)**

Controls & strata dummies No No

No Yes

Observations 533 533

326 308

Clusters 43 43

42 41








All All Unsafe

Unsafe

Intercept 0.07 (.027)*** 0.29 (.035)*** 0.23 (0.06)***

0.28 (0.19)***

+Gradient added −0.09 (.05)* 0.12 (0.11)

0.11 (0.07)

Unsafe (As> 50ppb) 0.28 (.043)*** −

−

High As (As> 100ppb) 0.24 (0.07)***

.22 (0.05)***

Gradient×High As −0.30 (0.12)**

−0.26 (0.10)**

Controls & strata dummies No No No

Yes

Observations 533 533 326

308

Clusters 43 43 42

41








All All Unsafe Unsafe

Intercept 0.07 (.027)*** 0.29 (.035)*** 0.23 (0.06)*** 0.28 (0.19)***+Gradient added −0.09 (.05)* 0.12 (0.11) 0.11 (0.07)Unsafe (As> 50ppb) 0.28 (.043)*** − −

High As (As> 100ppb) 0.24 (0.07)*** .22 (0.05)***Gradient×High As −0.30 (0.12)** −0.26 (0.10)**

Controls & strata dummies No No No Yes

Observations 533 533 326 308Clusters 43 43 42 41







Non-parametric regressions show striking pattern

(A) Arsenic level (ppb)

Lower bound of 90% C.I. Upper bound of 90% C.I. E(Switching | As, Binary)

50 100 150 200 250 300

0

.2

.4

.6

.8

(B) Arsenic level (ppb)

Lower bound of 90% C.I. Upper bound of 90% C.I. E(Switching | As, Gradient)

50 100 150 200 250 300

0

.2

.4

.6

.8

(C) Arsenic level (ppb)

E(Switching | As, Gradient) E(Switching | As, Binary)

50 100 150 200 250 3000

.2

.4

.6

.8

(D) Arsenic level (ppb)

Lower bound of 90% C.I. Upper bound of 90% C.I. E(Sw.|As,Grad.)-E(Sw.|As,Bin.)

50 100 150 200 250 300-1

-.5

0

.5

1


Conclusions & Limitations

Surprisingly large impacts, given relatively subtle differences betweenmessage scripts (Binary vs. Binary+Gradient)

More switching for As ∼50 makes sense

‘gradient’ likely to ‘worry’ those close to threshold)

But why does the richer message decrease switching among users of moreunsafe wells?

Also no evidence of ‘gradient’ leading to ‘better switches’, but informationon As levels of new source is limited.Survey-elicited risk perception data broadly consistent with switchingpatternsAlso some (mild) evidence that ‘gradient’ led to poorer understanding of risksResults survive when we control for characteristics likely to matter forswitching (e.g. distance from nearest safe well, well ownership etc.)

Search for more effective ways to provide As information still open, alsoconsidering potentially hazardous ‘side effects’ such as return of diarrhealdisease (van Geen et al 2010, Field et al. 2011).







Also no evidence of ‘gradient’ leading to ‘better switches’, but informationon As levels of new source is limited.

Survey-elicited risk perception data broadly consistent with switchingpatternsAlso some (mild) evidence that ‘gradient’ led to poorer understanding of risksResults survive when we control for characteristics likely to matter forswitching (e.g. distance from nearest safe well, well ownership etc.)








Also no evidence of ‘gradient’ leading to ‘better switches’, but informationon As levels of new source is limited.Survey-elicited risk perception data broadly consistent with switchingpatterns

Also some (mild) evidence that ‘gradient’ led to poorer understanding of risksResults survive when we control for characteristics likely to matter forswitching (e.g. distance from nearest safe well, well ownership etc.)








Also no evidence of ‘gradient’ leading to ‘better switches’, but informationon As levels of new source is limited.Survey-elicited risk perception data broadly consistent with switchingpatternsAlso some (mild) evidence that ‘gradient’ led to poorer understanding of risks

Results survive when we control for characteristics likely to matter forswitching (e.g. distance from nearest safe well, well ownership etc.)








Also no evidence of ‘gradient’ leading to ‘better switches’, but informationon As levels of new source is limited.Survey-elicited risk perception data broadly consistent with switchingpatternsAlso some (mild) evidence that ‘gradient’ led to poorer understanding of risksResults survive when we control for characteristics likely to matter forswitching (e.g. distance from nearest safe well, well ownership etc.)



Social Capital as an Instrument for Common Pool...

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